Characterization and Prediction of Issue-Related Risks in Software Projects

Morakot Choetkiertikul, Hoa Khanh Dam, Truyen Tran, Aditya Ghose
2015 2015 IEEE/ACM 12th Working Conference on Mining Software Repositories  
Identifying risks relevant to a software project and planning measures to deal with them are critical to the success of the project. Current practices in risk assessment mostly rely on high-level, generic guidance or the subjective judgements of experts. In this paper, we propose a novel approach to risk assessment using historical data associated with a software project. Specifically, our approach identifies patterns of past events that caused project delays, and uses this knowledge to
more » ... risks in the current state of the project. A set of risk factors characterizing "risky" software tasks (in the form of issues) were extracted from five open source projects: Apache, Duraspace, JBoss, Moodle, and Spring. In addition, we performed feature selection using a sparse logistic regression model to select risk factors with good discriminative power. Based on these risk factors, we built predictive models to predict if an issue will cause a project delay. Our predictive models are able to predict both the risk impact (i.e. the extend of the delay) and the likelihood of a risk occurring. The evaluation results demonstrate the effectiveness of our predictive models, achieving on average 48%-81% precision, 23%-90% recall, 29%-71% F-measure, and 70%-92% Area Under the ROC Curve. Our predictive models also have low error rates: 0.39-0.75 for Macroaveraged Mean Cost-Error and and 0.7-1.2 for Macro-averaged Mean Absolute Error.
doi:10.1109/msr.2015.33 dblp:conf/msr/ChoetkiertikulD15 fatcat:b4cmybw2urg7jii6l2sqjxomqi